整合生成性AI和机器学习分类器来解决异构的MCGDM:员工流失预测的案例
Hagar G Abu-Faty1, Ahmed Kafafy2, Mohiy M Hadhoud3
1Data Science Department, Faculty of Artificial Intelligence, Minufiya University, Shebeen El-Kom, Egypt. hagar.gamal@ai.menofia.edu.eg.
Scientific reports
|May 5, 2025
概括
预测员工流失对于企业的稳定至关重要. 这项研究整合了生成人工智能和决策模型,以准确预测员工流动,改进员工保留策略.
科学领域:
- 企业管理 企业管理
- 人工智能的人工智能
- 决策科学 决策科学 决策科学
背景情况:
- 员工流失严重影响组织的生产力,效率和运营成功.
- 高周转率会带来大量的招聘和培训成本,破坏工作流程并阻碍稳定性.
- 预测和减轻员工流失是持续组织增长的关键问题.
研究的目的:
- 介绍一种用于在异质环境中预测员工流失的新方法.
- 将员工流失预测作为一个多重标准组决策 (MCGDM) 问题.
- 整合生成性AI,传统的MCGDM技术和机器学习,以提高预测准确度.
主要方法:
- 数据收集和预处理.
- 利用生成性人工智能 (ChatGPT-4) 模拟各种流失相关领域的专家个人资料.
- 应用分析层次流程 (AHP) 进行标准权重和采用类似于理想解决方案的订单偏好技术 (TOPSIS) 进行员工排名.
- 在MCGDM衍生排名上使用机器学习分类器 (神经网络,梯度提升,随机森林) 进行预测建模.
主要成果:
- 综合方法有效地处理复杂环境中的员工流失预测.
- 基于MCGDM的排名减少了后续机器学习预测的计算复杂性.
- 神经网络,梯度增强和随机森林在预测员工流失方面表现出卓越的准确性.
结论:
- 拟议的方法提供了一个可扩展的,数据驱动的解决方案,用于员工流失预测.
- 将先进的人工智能与传统决策框架相结合,为复杂的组织挑战提供了强大的方法.
- 这种方法提高了组织主动解决和减轻员工流动的能力.
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